VISUALISED: Marriage rates

Posted on March 31, 2011 by

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By JENNY STEVENS

As the Office for National Statistics (ONS) published the latest marriage rates for England and Wales yesterday, Seeing Stories thought it would be a good opportunity to experiment with a simple infographic from a large data set.

The full data is available from the ONS website, or on the Guardian Data Blog. We used visualisation tool Many Eyes to make two simple line graphs. The line graph is an easy way to visualise this data, which is pretty meaningless as a long list of numbers. There are many more data sets in this area, such as divorce rates, number of cohabiting couples, number of first marriages etc. which build a bigger picture of the state of marriage, and would make a much more comprehensive data visualisation. But for now, we’ll keep it simple.

The data is for the General Marriage Rate, which was first calculated in 1862. It’s the number of men or women marrying per 1,000 unmarried men or women. This is different to the number of registered marriages, because it takes into account changes in the size of the unmarried population.

Marriage Rates 1862-2009 (Number of people marrying per 1,000 unmarried people)

From the graph, you can see that the marriage rate rose steadily from the 1840s to the 1940s, with peaks and dips around the two world wars. Around the late 1940s to late 1950s, the marriage rate declines, before increasing until the 1970s.

Over the last three decades, the marriage rate has fallen dramatically, to the lowest since records began. The ONS says that this is because men and women are waiting to get married, or not marrying at all. It also says that the rise in couples cohabitating before or instead of marrying is a contributing factor.

Male and Female Marriage Rates 1862-2009 (number of men or women marrying per 1,000 unmarried men or women. Men - purple (top line), Women - navy blue (bottom line))

In this second graph, we used the comparative data for men and women. The graph spikes are obviously very similar, but the rate is continually lower for women. We’ve tried to find information on why this is, but haven’t had much luck. Are there any data experts out there who can tell us why?

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